Lisandro Milocco

DDLS Fellow, Stockholm University

Key publications

Milocco, L., & Uller, T. (2024). Utilizing developmental dynamics for evolutionary prediction and control. Proceedings of the National Academy of Sciences, 121(14), e2320413121.

Milocco, L., & Uller, T. (2023). A data‐driven framework to model the organism–environment system. Evolution & Development, 25(6), 439-450.

Milocco, L., & Salazar-Ciudad, I. (2022). A method to predict the response to directional selection using a Kalman filter. Proceedings of the National Academy of Sciences, 119(28), e2117916119.

Milocco, L., & Salazar-Ciudad, I. (2022). Evolution of the G matrix under nonlinear genotype-phenotype maps. The American Naturalist, 199(3), 420-435.

Milocco, L., & Salazar-Ciudad, I. (2020). Is evolution predictable? Quantitative genetics under complex genotype-phenotype maps. Evolution, 74(2), 230-244.

How has evolution generated the extraordinary diversity of life on Earth?

At the heart of this question lies evolvability—the ability of organisms to generate variation. Our research investigates the foundations of evolvability while exploring its potential applications, with a particular focus on evolutionary prediction: can we use a deeper understanding of evolvability to predict and, potentially, control evolutionary processes?

To understand evolvability, we focus on development—the process by which variation in genetic and environmental factors gives rise to differences in morphology, physiology, and behavior. Specifically, we approach development as a dynamical system that changes over time according to specific developmental rules. By applying computational and mathematical tools from dynamical systems theory, we aim to uncover the principles that govern evolvability and shape the evolutionary potential of organisms.

Our research themes include:

  • Exploring how perturbations of different origin, such as genetic and environmental, affect development and create phenotypic variation.
  • Data-driven modeling of development for evolutionary prediction and control.
  • Dimensionality reduction generated by development.
  • Developmentally-informed quantitative genetics.

Last updated: 2025-03-03

Content Responsible: Hampus Pehrsson Ternström(hampus.persson@scilifelab.uu.se)